Tag

AI

AI for Experience: From Customers to Stakeholders | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog, Customer Experience

Everest Group’s digital services research indicates that 89 percent of enterprises consider customer experience (CX) to be their prime digital adoption driver. But we believe the digital experience needs to address all stakeholders an enterprise touches, not just its customers. We touched on this topic in our Digital Services – Annual Report 2018, which focuses on digital operating models.

Indeed, SAP’s recent acquisition of Qualtrics and LinkedIn’s acquisition of Glint indicates the growing importance of managing not only CX, but also the digital experience of employees, partners, and the society at large.

AI Will Usher in the New Era of the Digital Experience Economy

Given the deluge of data from all these stakeholders and the number of parameters that must be addressed to deliver a superior experience, AI will have to be the core engine powering this digital experience economy. It will allow enterprises to build engaging ecosystems that evolve, learn, implement continuous feedback, and make real time decisions.

 

AI’s Potential in Transforming CX is Vast

Today, most enterprises narrowly view the role of AI in CX as implementing chatbots for customer query resolution or building ML algorithms on top of existing applications to enable a basic level of intelligence. However, AI can be leveraged to deliver very powerful experiences including: predictive analytics to pre-empt behaviors; virtual agents that can respond to emotions; advanced conversational systems to drive human-like interactions with machines; and even to deliver completely new experiences by using AI in conjunction with other technologies such as AR/VR, IoT, etc.

Digital natives are already demonstrating these capabilities. Netflix delivers hyper personalization by providing seemingly as many versions as its number of users. Amazon Go retail stores use AI, computer vision, and cameras to deliver a checkout free experience. And the start-up ecosystem is rampant with examples of cutting-edge innovations. For instance, HyperSurfaces is designing next-gen user experiences by using AI to transform any object to user interfaces.

But focusing just on the customer experience is missing the point, and the opportunity.

 AI in the Employee Experience

AI can, and should, play a central role in reimagining the employee journey to promote engagement, productivity, and safety. For example, software company Workday analyzes 60 data points to predict attrition risk. Humanyze enables enterprises to ascertain if a particular office layout supports teamwork. If meticulously designed and tested, AI algorithms can assist in employee hiring and performance management. With video analytics and advanced algorithms, AI systems can ensure worker safety; combined with automation, they can free up humans to work on more strategic tasks.

AI in the Supplier and Partner Experience

Enterprises also need to include suppliers and other partners in their experience management strategy. Using predictive analytics to automate inventory replenishment, gauge supplier performance, and build channels for two-way feedback are just a few examples. AI will play a key role in designing systems that not only pre-empt behaviors/performance but also ensure automated course correction.

AI in the Society Experience

Last but not least, enterprises cannot consider themselves islands in the environment in which they operate. They must realize that experience is as much about reality as about perception. Someone who has never engaged with an enterprise may have an “experience” perception about that organization. Some organizations’ use of AI is clearly for “social good.” Think smart cities, health monitoring, and disaster management systems. But even organizations that don’t have products or services that are “good” for society must view the general public as an important stakeholder. For example, employees at Google vetoed the company’s decision to engage with the Pentagon for use of ML algorithms for military applications. Similarly, employees at Microsoft raised concerns over the company’s involvement with Immigration and Customs Enforcement in the U.S.  AI can be leveraged to predict any such moves by pre-empting the impact that a company’s initiatives might have on society at large.

Moving from Customer to Stakeholder Experience

As organizations make the transition to an AI-enabled stakeholder experience, they must bear in mind that a piecemeal approach will not work. This futuristic vision will have to be supported by an enterprise-wide commitment, rigorous and meticulous preparation of data, ongoing monitoring of algorithms, and significant investment. They will have to cover a lot of ground in reimagining the application and infrastructure architecture to make this vision a distinctive reality.

What has been your experience leveraging AI for different stakeholders’ experiences? Please share with us at [email protected] and [email protected].

 

Investments in Healthcare AI Will Quadruple by 2020, According to Everest Group | Press Release

By | Press Releases

New research predicts US$6 billion investment will drive innovations in patient identity verification, opioid abuse detection and individually tailored healthcare.

Healthcare organizations are pouring billions into embedded AI across the value chain, driving an estimated quadrupling of AI investments in the next three years, according to Everest Group. The firm predicts that healthcare AI investments will grow from US$1.5 billion in 2017 to exceed US$6 billion by 2020, representing a compound annual growth rate of 34 percent.

While AI is a relatively new area in the healthcare space and its adoption is in the nascent stage, digitalization of healthcare is accelerating healthcare enterprises’ interest in AI. AI has the potential to transform healthcare processes and dramatically reduce costs and improve efficiencies.

For example, healthcare payers are leveraging AI for product development, policy servicing, network management and claims management. Examples include:

  • Use of fingerprints, eye texture, voice, hand patterns and facial recognition to reduce the time taken for customer verification
  • Leveraging of machine learning with integrated claims data and analytics to detect opioid use patterns that suggest misuse
  • AI-powered wearable devices and mobile applications to help customers with personalized advice
  • Chatbots and virtual assistants to predict the right answer to standard customer inquiries and assist customers in navigating through the insurance plan selection process.

Currently, the area where payers are adopting AI to the greatest extent is in care management.

Likewise, the highest adoption of AI by healthcare providers is for care and case management. Providers also are employing AI tools to:

  • collaborate more effectively with patients
  • reduce the time required for aggregating, storing, and analyzing patients’ data
  • streamline workflows
  • monitor patients remotely
  • detect diseases faster and more accurately
  • come up with better treatments.

These findings and more are discussed in Everest Group’s recently published report, “Dr. Robot Will See You Now: Unpacking the State of Artificial Intelligence in Healthcare – 2019.” The firm has analyzed the market from the vantage point of 27 leading healthcare enterprises and closely examined the distinctive attributes of the leaders, who are far ahead of the other industry participants in terms of AI capability maturity. The report identifies best practices, illustrates the impact generated, and offers proposed a roadmap for market stakeholders.

***Download a complimentary abstract of this report here. ***

“While healthcare enterprises are still in the nascent stages of AI adoption, the scale of opportunity in AI demands C-level vision,” said Abhishek Singh, vice president of Information Technology Services at Everest Group. “AI presents unique opportunities for healthcare enterprises – allowing them to improve customer experience, achieve operational efficiency, enhance employee productivity, cut costs, accelerate speed-to-market, and develop more personalized products. In the case of the leading healthcare organizations, their CEOs and CIOs are acknowledging the transformative power of AI, rapidly building appropriate AI strategies, and building a robust, overarching business plan to harness its benefits.”

Additional key findings:

  1. Nearly two-thirds of spending on AI in healthcare is driven by North America. The North American market is also expected to be the fastest growing during the next five years, driven by regulatory mandates for use of electronic health records, increasing focus on precision medicine and a strong presence of service providers engaged in developing AI solutions for healthcare.
  2. Around 75 percent of all AI initiatives in healthcare are still driven by large enterprises as most mid- and small-sized firms are taking a wait-and-see approach.
  3. With a boom in enterprise AI, talent scarcity has become one of the biggest leadership challenges in implementing and evolving AI capabilities.
  4. Application of machine learning (ML) for structured data and natural language processing (NLP) for unstructured information have become mainstream in the healthcare industry.
  5. Cognitive technologies are expected to play an important part in health plans’ technology strategies going forward. Also, providers are looking to increasingly leverage deep learning to explore more complex, non-linear patterns in data, such as that found in research papers, doctors’ notes, textbooks, clinical reports, health histories, X-rays and CT and MRI scans.

Using AI to Build, Test, and Fight AI: It’s Disturbing BUT Essential | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog

Experts and enterprises around the world have talked a lot about the disturbing concept of AI being used to build and test AI systems, and challenge decisions made by those systems. I wrote a blog on this topic a while back.

Disquieting as it is, our AI research makes it clear that AI for AI with increasingly minimal human intervention has moved from concept to reality.

Here are four key reasons this is the case.

Software is Becoming Non-deterministic and Intelligent

Before AI emerged, organizations focused on production support to optimize the environment after the software was released. But those days are going to be over soon, if they aren’t already. The reality is that today’s increasingly dynamic software and Agile/DevOps-oriented environments require tremendous automation and feedback loops from the trenches. Developers and operations teams simply cannot capture and analyze the enormous volume of needed insights. They must leverage AI intelligence to do so, and to enable an ongoing interaction channel with the operating environment.

Testing AI Biases and Outcomes is not Easy

Unlike traditional software with defined boundary conditions, AI systems have very different edge scenarios. And AI systems need to negate/test all edge scenarios to make sense of their environment. But, as there can be millions of permutations and combinations, it’s extremely difficult to manually assure or use traditional automation to test AI systems for data biases and outcomes. Uncomfortable as it may be, AI-layered systems must be used to test AI systems.

The Autonomous Vehicle Framework is Being Mirrored in Technology Systems

The L0-L5 autonomous vehicle framework proposed by SAE International is becoming an inspiration for technology developers. Not surprisingly, they want to leverage AI to build intelligent applications that can have autonomous environments and release. Some are even pushing AI to build the software itself. While this is still in its infancy, our research suggests that developers’ productivity will improve by 40 percent if AI systems are meaningfully leveraged to build software.

The Open Source Ecosystem is Becoming Indispensable

Although enterprises used to take pride in building boundary walls to protect their IP and using best of breed tools, open source changed all that. Most enterprises realize that their developers cannot build an AI system on their own, and now rely on open source repositories. And our research shows that 20-30 percent of an AI system can be developed by leveraging already available code. However, scanning these repositories and zeroing in on the needed pieces of code aren’t tasks for the faint hearted given their massive size. Indeed, even the smartest developers need help from an AI intelligent system.

There’s little question that using AI systems to build, test, and fight AI systems is disconcerting. That’s one of the key reasons that enterprises that have already adopted AI systems haven’t yet adopted AI to build, test, and secure them. But it’s an inevitability that’s already knocking at their doors. And they will quickly realize that reliance on a “human in the loop” model, though well intentioned, has severe limitations not only around the cost of governance, but also around the sheer intelligence, bandwidth, and foresight required by humans to govern AI systems.

Rather than debating its merit or becoming overwhelmed with the associated risks, enterprises need to build a governing framework for this new reality. They must work closely with technology vendors, cloud providers, and AI companies to ensure their business does not suffer in this new, albeit uncomfortable, environment.

Has your enterprise started leveraging AI to build, test, or fight AI systems? If so, please share your experiences with me at [email protected].

Artificial Intelligence without the Hype: The Real Role of AI in Business Today | In the News

By | In The News

Artificial Intelligence (AI) has been the stuff of science fiction for decades and more recently has become a rampant buzzword in business media headlines. But CIOs need to know if there are realities amid the hype. Is AI actually delivering value and not just Proofs of Concept? In other words, are the business bona fides showing up yet?

Click to read the full article

Also available online at Applications Europe Magazine

Artificial Intelligence-led Alert Correlation | Webinar

By | Webinars

Wednesday, September 12, 2018 | 11 a.m. EST | Hosted by GAVS Technologies with featured speaker, Ashwin Venkatesan, Practice Director, Everest Group

Register Now

AIOps is poised to become the next big thing in IT management. By maintaining the fidelity of data and generating insights, it has ability to influence business decisions. In the era of digital transformation, the adoption of AIOps is imperative for businesses with dynamic and complex IT environments.

Join GAVS for a webinar on “Artificial Intelligence-led Alert Correlation – Enabling the Journey towards Zero Incidents” featuring expert guest speaker Ashwin Venkatesan.

Why should you attend?

  • To know about the increasing complexities within the current enterprise IT operations model
  • To understand how AI-led alert correlation can deliver a step change in IT operations and business performance
  • To learn about the best practices for enterprises while adopting AI-led alert correlation solutions
  • To explore GAVel – GAVS’ AIOps platform for intelligent alert correlation

Guest Speaker

Ashwin Venkatesan, Practice Director, Everest Group

Register to attend